Optical coherence tomography (OCT) is a noninvasive, noncontact imaging modality that uses coherence gating to obtain high-resolution cross-sectional images of tissue microstructure. In Fourier domain OCT (FD-OCT), the interferometric signal between light from a reference and the back-scattered light from a sample point is recorded in the frequency domain rather than the time domain. After a wavelength calibration, a one-dimensional Fourier transform is taken to obtain an A-line spatial distribution of the object scattering potential. The spectral information discrimination in FD-OCT is accomplished either by using a dispersive spectrometer in the detection arm in the case of spectral-domain OCT (SD-OCT) or rapidly tuning a swept laser source in the case of swept-source OCT (SS-OCT). The axial or depth resolution of the FD-OCT system is determined by the actual spectral width recorded and used for reconstruction. The axial range over which an OCT image is taken (imaging depth, scan depth or imaging range) is determined by the sampling interval or resolution of the optical frequencies recorded by the OCT system.
Functional OCT can provide important clinical information that is not available in the typical intensity based structural OCT images. There have been several functional contrast enhancement methods including Doppler OCT, Phase-sensitive OCT measurements, Polarization Sensitive OCT, etc. Integration of functional extensions can greatly enhance the capabilities of OCT for a range of applications in medicine. One of the most promising functional extensions of OCT has been the field of OCT angiography which is based on flow contrast. The field of OCT angiography has generated a lot of interest in the OCT research community during the last few years. There are several flow contrast techniques in OCT imaging that utilize inter-frame change analysis of the OCT intensity or phase-resolved OCT data. One of the major applications of such techniques has been to generate en face vasculature images of the retina. En face images are typically generated from three dimensional data cubes by summing pixels along a given direction in the cube, either in their entirety or from sub-portions of the data volume (see for example U.S. Pat. No. 7,301,644). Visualization of the detailed vasculature using OCT could enable doctors to obtain new and useful clinical information for diagnosis and management of eye diseases in a non-invasive manner. Fluorescein angiography and indocyanine green (ICG) angiography are currently the gold standards for vasculature visualization in the eye. However, the invasiveness of the approach combined with possible complications (allergy to dyes, side effects) make it an unsuitable technique for widespread screening applications in ophthalmic clinics.
Vasculature can be visualized by OCT using the effect of motion caused by blood flow on the backscattered light. Doppler-OCT has been used for more than a decade to provide contrast due to blood flow. However, Doppler OCT, despite being capable of quantifying blood flow, has several limitations such as limited dynamic range and dependence of Doppler signal on the angle of probe beam and flow direction. In addition, the pulsatile nature of blood flow can also affect the Doppler signal. Chen et al. demonstrated the use of the Doppler variance technique for the first time to obtain vasculature mapping (see for example Y. Zhao et al. Doppler standard deviation imaging for clinical monitoring of in vivo human skin blood flow,” Optics Letters 25, 1358-1360 (2000)). Doppler variance, while incapable of quantifying the blood flow, is less sensitive to the Doppler angle and the pulsatile nature of the blood flow. Hence Doppler variance provides better detection for the location of the blood flow. Makita et al. used phase-resolved Doppler OCT to perform OCT vasculature mapping of the human retina (S. Makita et al. “Optical Coherence Angiography,” Optics Express 14, 7821-7840 (2006)). R. K. Wang et al. developed a technique, optical microangiography (OMAG), that applies a constant modulation frequency to the interferograms formed between reference and sample beams to separate the static and moving elements using mathematical properties of Hilbert and Fourier transformations applied on real valued interferometric data. This resulted in separation of the vasculature image (due to motion of blood flow) and the tissue image (see for example R. K. Wang et al. “Three dimensional optical angiography,” Optics Express 15, 4083-4097 (2007) and L. An et al., “In vivo volumetric imaging of vascular perfusion within human retina and choroids with optical microangiography,” Optics Express 16, 11438-11452 (2008)). Wang et al. claimed improved sensitivity for microvasculature flow measurements using a technique called ultrahigh sensitive OMAG (UHS-OMAG). In this technique, Wang et al. applied the OMAG technology along the slow scan axis, i.e. the time separation between two measurements was now determined by B-frame rate rather than A-scan rate (see for example R. K. Wang et al., “Depth-resolved imaging of capillary networks in retina and choroid using ultrahigh sensitive optical microangiography,” Optics Letters, 35(9), 1467-1469 (2010) and L. An et al., “Ultrahigh sensitive optical microangiography for in vivo imaging of microcirculations within human skin tissue beds,” Optics Express, 18(8), 8220-8228 (2010)). However, this method requires higher post-processing computational load and some of the UHS-OMAG methods added hardware complexity to the system. Fingler et al. performed Doppler or phase variance based detection by comparing the phase data at the same location from multiple B-scans or frames (see for example J. Fingler et al., “Mobility and transverse flow visualization using phase variance contrast with SD-OCT,” Optics Express 15, 12636-12653 (2007), J. Fingler et al., “Volumetric microvascular imaging of human retina using OCT with a novel motion contrast technique,” Optics Express 17, 22190-22200 (2009) and US Patent Publication No. 2008/0025570). Because inter-frame analysis was used instead of comparing subsequent A-scans, Fingler et al. produced improved vasculature images compared to the results obtained by Chen et al. The Inter-frame analysis used by Fingler et al. allowed increased time difference between two OCT measurements at the same location and hence increased the sensitivity to slower flow. Inter-frame analysis using OCT B-scan images to measure small displacements was also previously used for research studies in OCT elastography (see for example J. M. Schmitt, “OCT elastography: imaging microscopic deformation and strain of tissue,” Optics Express 3, 199-211 (1998) and S. J. Kirkpatrick, R. K. Wang, and D. D. Duncan, “OCT-based elastography for large and small deformations,” Optics Express 14, 11585-11597 (2006). Fingler's inter-frame phase variance analysis method relies only on the phase information in the OCT data to detect motion. One of the limitations of phase only methods is that phase signals have increased error in measurements at low backscattered signal intensity. The OCT data obtained from the light backscattered from the moving scattering particles has variations in intensity as well as the phase. Mariampillai et al. confirmed this by performing vasculature imaging based on inter-frame speckle variance analysis (see for example A. Mariampillai et al. Optimized speckle variance OCT imaging of microvasculature,” Optics Letters 35, 1257-1259 (2010)).